Forthcoming in Applied Financial Economics

♣ We are grateful to the editor, Mark Taylor, and one anonymous referee for useful comments and suggestions on anearlier version of the paper. The first author gratefully acknowledges partial financial support from the Faculty ofGraduate Studies, York University. The usual disclaimer applies.‡ Corresponding author: Phone: (504) 280-6163, Fax: (504) 280-6397. Email: mhassan@uno.edu Time-Varying Volatility and Equity Returns in Bangladesh Stock Market

AbstractThis paper empirically examines the time-varying risk return relationship and the impact of institutionalfactors such as circuit breaker on volatility for the emerging equity market of Bangladesh (namely TheDhaka Stock Exchange (DSE)) using daily and weekly stock returns. The DSE equity returns shownegative skewness, excess kurtosis and deviation from normality. The returns display significant serialcorrelation suggesting stock market inefficiency. The results also show a significant relationship betweenconditional volatility and stock returns, but the risk-return parameter is found to be sensitive to choice ofsamples and frequencies of data. Overall, the coefficient of the risk-return parameter is negative andstatistically significant. While this result is not consistent with the portfolio theory, it is possibletheoretically in emerging markets as investors may not demand higher risk premia if they are better ableto bear risk at times of particular volatility (Glosten, Jagannathan and Runkle, 1993). While lock-in didnot have any overall impact on stock volatility, the imposition of a circuit breaker has contributedsignificantly to the volatility of realized returns. As a policy to improve the operation of capital markettimely disclosure and dissemination of information to the shareholders and investors on the performanceof listed companies should be emphasized.

While empirical tests of return-volatility behavior are plentiful for developed stock markets, the focus ondeveloping and emerging stock markets has only begun in recent years. The interest in these emergingmarkets has arisen from the increased globalization and integration of the world economy in general andthat of financial markets in particular. The globalization and integration of these markets has createdenormous opportunities for domestic and international investors to diversify their portfolios across theglobe. As a result, rigorous empirical studies examining the efficiency and other characteristics of thesemarkets would be of great benefit to investors and policy makers at home and abroad. A number of papers, Poshakwale and Murinde (2001), Siourounis (2002), Bologna and Cavallo(2002), and Gonzalez et al. (2003), have examined the return-volatility behavior of a number of emergingmarket economies.1 Poshakwale and Murinde (2001) examine the stock market volatility of two emergingstock markets (Hungary and Poland) using daily indexes. Using GARCH-M technique, the authors findthat volatility is roughly persistent in both markets although it declined after 1995 for Poland due toimproved market integration with the UK and German stock markets. Siourounis (2002) empiricallyexamines the efficiency of Athens stock exchange using daily returns data. Using various GARCH typemodels he finds that the market is not efficient and factors such as political instability influences the pricebehavior of the stock. Bologna and Cavallo (2002) apply the GARCH technique to examine therelationship between stock index futures and stock market volatility for the Italian stock exchange. Usingdaily returns data the authors find that stock index futures contribute to significant reduction in the stockmarket volatility. Gonzalez et al. (2003) fit a GARCH(1, 1) model to study the Mexican market volatilityusing weekly equity returns. Results show that volatility in equity returns is primarily explained by thepresence of the statistical outlier in the data. While the relationships between volatility and return has been examined for a number of emergingmarkets, very little has done for a frontier capital market like Bangladesh, primarily because of lack ofdata. Hassan and Maroney (2004) examined the issue of non-linearity and thin trading as a test for marketefficiency in the context of Bangladesh (i.e., DSE). The authors fit a cubic function of stock return and anAR(1) return process, respectively, for non-linearity and thin trading using daily return data over theperiod September 1986 through November 1999. Results show that the evidence of non-linearity cannotbe rejected even after correcting for thin trading in the sample. While the Hassan and Maroney (2004)study is important in determining market efficiency, the questions of stock market volatility, persistenceof volatility, and risk premia in the stock market have not been addressed in their study.

1 Other important contributions include Haque et al. (2001), Harvey (1995a,b), Bekaert (1995), Bekaert and Harvey(1995), and Choudhury (1996), to mention a few.

2 The aim of this paper is twofold. First, we examine the return distributions and stochastic processes ofsuch distributions in the stock market of Bangladesh following the deregulation and opening up of itscapital market to foreign investors in the 1990’s. Second, we examine the impact of institutional factorssuch as circuit breakers and lock-ins on stock market volatility. In particular, the paper examines the time-varying risk return relationship for this emerging equity market using a data set of daily and weeklyreturns. This long data series allows us to examine the impact of capital market opening on the volatilityof the Bangladesh stock market, the associated risk premia, and the persistence of shocks to stock marketvolatility before, during, and after the capital market liberalization that began in 1990. We use GARCH-type models introduced by Engle et al. (1987) and Bollerslev (1986) to examine the time-varying risk-return relationship. The GARCH models are capable of incorporating a number of widely observedbehaviors of stock prices such as leptokurtosis, skewness, and volatility clustering. This study is important for a number of reasons. First, to the best of our knowledge, this is the firststudy of this kind for the Bangladesh stock market. Second, it utilizes a unique daily and weekly dataseries, which have not been utilized in previous studies. Third, the results of this study will be of interestto academics, policy makers and investors both at home and abroad. Finally, it may also be useful forinternational organizations (such as the World Bank) and foreign governments who are interested in thedevelopment of capital markets in emerging countries. The rest of the paper is organized as follows. Section 2 provides a brief overview of the Dhaka StockExchange. Section 3 discusses methodology and sources of data. Section 4 discusses the statisticalproperties of the stock prices and returns in the Dhaka Stock Exchange. Section 5 analyzes the empiricalfindings of time-varying risk-return behavior of stock prices and returns using GARCH-type framework.Section 6 concludes the paper.

2. The Dhaka Stock Exchange (DSE): A Brief Description

On April 28, 1954 the DSE was first incorporated as the East Pakistan Stock Exchange AssociationLimited. However, formal trading began in 1956 with 196 securities listed on the DSE with a total paidup capital of about Taka 4 billion (Chowdhury, 1994). On June 23, 1962 it was renamed as Dhaka StockExchange (DSE) Limited. After 1971, the trading activities of the Stock Exchange remained suppresseduntil 1976 due to the liberation war and the economic policy pursued by the ruling government. Thetrading activities resumed in 1976 with only nine companies listed having a paid up capital of only US$20million (Chowdhury, 1994). As of June 30, 2003, there were 260 securities listed on the DSE with amarket capitalization of US$ 1.25 billion (see Table 1). DSE is registered as a Public Limited Company and its activities are regulated by its Articles ofAssociation and its own rules, regulations, and by-laws along with the Bangladesh Securities and

3Exchange Ordinance, 1969; the Companies Act, 1994; and the Securities and Exchange Commission Act,1993. As per the DSE Article 105B, its management is separated from the Council which is consists of 24members of which 12 are elected from the members and the other 12 are nominated as non-members fromdifferent apex bodies (SEC, 2003). The executive power of the DSE is vested with the Chief ExecutiveOfficer (CEO). The CEO is appointed by the Board with the approval of the Securities and ExchangeCommission (SEC). At present, the DSE has a staff of 115 people. The Dhaka Stock Exchange is a selfregulated non-profit organization. It has provisions for 500 members though at present the number is 195.Membership is open to foreigners as well. Trading is done through an automated on-line system every day except Friday and other governmentholidays. There are four markets in the system: (1) Public Market: only trading of market lot share is donehere through automatic matching. (2) Spot Market: spot transactions are done here through automaticmatching which must be settled within 24 hours. (3) Block Market: a place where bulk quantities ofshares are traded on a pick and fill basis. (4) Odd Lot Market: odd lot scripts are traded here based on apick and fill basis. All the transactions of a day in public market are settled after netting, and clearedthrough the DSE Clearing House on the 3rd and 5th working day respectively, calculated from date oftrading. Members shall be allowed to carry out transactions of foreign buyers and/or sellers involving acustodian bank to be settled directly between the member through the custodian bank within the fifth daysubsequent to the trading day, i.e., T + 5 in respect of the transactions carried out on each trading day withintimation to the clearing house.

3. Methodology and Data:

3.1. Methodology:

The study examines the distribution of equity returns by dividing the sample period into two subperiods; periods before and after the market was opened to international investors. Return distributionsare studied by comparing the descriptive statistics of the Dhaka Stock Exchange Index (DSEI). In order toexamine the stochastic process over the study period, we employ models of conditional variances usingthe Generalized Autoregressive Conditional Heteroskaedasticity (GARCH) formulation. The GARCHapproach allows for an empirical assessment of the relationship between risk and returns in a setting thatis consistent with the characteristics of leptokurtosis and volatility clustering observed in emerging stockmarkets. Moreover, conclusions regarding the predictability of returns based on the significance ofautocorrelation coefficients are valid only after controlling for the ARCH effects (Errunza et al. (1994)).

The Autoregressive Conditional Heteroskedasticity (ARCH) model introduced by Engle (1982) allowsthe variance of the error term to vary over time, in contrast to the standard time series regression models

4which assume a constant variance. Bollerslev (1986) generalized the ARCH process by allowing for a lagstructure for the variance. The generalized ARCH models, i.e. the GARCH models, have been found to bevaluable in modeling the time series behavior of stock returns (French et al. (1987), Akgiray (1989),Baillie and DeGennaro (1990), Koutmos (1992) and Koutmos et al. (1993)). Bollerslev (1986) allows theconditional variance to be a function of the prior period’s squared errors as well as of its past conditionalvariances.

The GARCH model has the advantage of incorporating heteroscedasticity into the estimationprocedure. All GARCH models are martingale difference implying that all expectations are unbiased. TheGARCH models are capable of capturing the tendency for volatility clustering in financial data. Volatilityclustering in stock returns implies that large (small) price changes follow large (small) price changes ofeither sign. Engle et al. (1987) provide an extension to the GARCH model where the conditional mean isan explicit function of the conditional variance. Such a model is known as the GARCH in the mean orGARCH-M model. Following Choudhury (1994) and Mecagni and Sourial (1999), stock returns can berepresented by the GARCH(p,q)-M model as follows:

(1) yt = ut + δ1 ht1/2 + εt,

(2) εt / Ψt-1 ∼ N(0, ht)

p q(3) ht = ω + ∑ β j ht − j + ∑α j (ε t − j ) 2 j =1 j =1

where yt is the stock return, ut is the mean of yt conditional on past information (Ψt-1), and thefollowing inequality restrictions ω>0, αj≥0, βj≥0 are imposed to ensure that the conditional variance (hi)is positive. The presence of ht1/2 in (1) provides a way to directly study the explicit trade off between riskand expected return. According to Chou (1988), the GARCH-M model provides a more flexibleframework to capture various dynamic structures of conditional variance and it allows simultaneousestimation of parameters of interest and hypotheses. The size and significance of αj indicates themagnitude of the effect imposed by the lagged error term (εt-1) on the conditional variance (ht). In otherwords, the size and significance of αj implies the existence of the ARCH process in the error term(volatility clustering).

The economic interpretation of the ARCH effect in stock markets has been provided within bothmicro and macro frameworks. According to Bollerslev et al. (1992, p.32) and other studies, the ARCH

5effect in stock returns could be due to the clustering of trade volumes, nominal interest rates, dividendyields, money supply, oil price index, etc. The significant influence of volatility on stock returns iscaptured by the coefficient of ht1/2(δ1) in (1). In other words, the coefficient δ1 represents the index ofrelative risk aversion (time-varying risk premium). A significant and positive coefficient δ1 implies thatinvestors trading stocks were compensated with higher returns for bearing higher levels of risk. Asignificant negative coefficient indicates that investors were penalized for bearing risk. According toBollerslev et al. (1992), the GARCH-M model provides a natural tool to investigate the linear relationshipbetween the return and variance of the market portfolio provided by Merton’s (1973, 1980) intertemporalCAPM.

Engle and Bollerslev (1986), Chou (1988), Bollerslev et al. (1992) show that the persistence of shocksto volatility depends on the sum of the α+β parameters. Values of the sum lower than unity imply atendency for the volatility response to decay over time. In contrast, values of the sum equal (or greater)than unity imply indefinite (or increasing) volatility persistence to shocks over time. However, asignificant impact of volatility on the stock prices can only take place if shocks to volatility persist over along time (Poterba and Summers, 1986).

The GARCH (p,q)-M model is estimated for Dhaka Stock Exchange Index (DSEI) using the Berndt,Hall, Hall and Hausman (1974) maximum likelihood method (henceforth, BHHH), as in other studiesbased on the same modeling methodology. First, we select a simple autoregressive specification of the utbased on the sequence of past stock returns. The Box-Jenkins method based on sample autocorrelationsand partial autocorrelations and sensitivity tests suggest a simple first-order autoregressive process AR(1)is a reasonable and parsimonious specification for the DSEI daily index. Second, we examine theresiduals from the conditioning AR(1) specification for the presence of GARCH effects based on ageneral-to-specific modeling strategy. This involves re-estimating jointly the AR(1)-GARCH (p,q)-Mmodel by the BHHH algorithm, starting from a GARCH (3,3) specification and eliminating insignificant(p,q) terms sequentially, in order of least significance. We employ both Ljung-Box and Breusch-Godfreylikelihood ration tests to check the specification of GARCH-M (1,1) model in this paper.

In a GARCH(1,1)-M model, the series εt is covariance stationary if the sum of α and β is significantlyless than unity. As the sum of α and β approaches unity, the persistence of shocks to volatility is greater.A GARCH-M(1,1) of the following form is used in this study:

(4) Yt = ut + δ1ht1/2 +εt

(5) εt|Ψt-1 ~ N(0,ht)

6(6) ht = α0 + α1ε2t-1+β1 ht-1

In order to examine the impact of circuit breakers and lock-ins on volatility, we estimate a GARCH(1,1)model so that the risk-return trade-off coefficient of GARCH-M model cannot influence the direction ofvolatility after the impositions of circuit-breakers and lock-ins. Consequently, we introduced the dummyof circuit-breakers and lock-ins directly in the conditional variance equation (see eq. 7) as an exogenousvariable. The GARCH(1,1) model uses the form of eq. (4)-(7) with a value of the risk-return trade-offcoefficient, δ1, equal to zero.

3.2. Data: Daily closing prices from the Dhaka Stock exchange are used for the period beginning 1September 1986 to 30 January 2002. The data are collected from two sources: data for the period 1September 1986 through 31 December 1993 were extracted from the various monthly bulletin of DSE(1993), while data for the period 1 January 1994 to 30 January 2002, were obtained electronically fromUnion Capital Limited,2 an investment company based in Dhaka, Bangladesh. In order to examinewhether our results are sensitive to the frequency of data, we used a five-day average of the daily data toconstruct weekly data. The DSEI index is composed of all traded stocks (value weighted) in the exchange.The index includes dividends.

4. Statistical Properties of the DSE Daily Returns

Table 2a and 2b provide the statistical characteristics of Dhaka Stock exchange for daily and weeklyreturns, respectively. We ran a Chow test to ascertain the actual break in the data set, and the Chow F-testresult confirms December 30, 1990 as a structural break in the data set. We divided the sample into twosub-periods: the pre-financial liberalization (Sept. 86-Jan. 2002) and the post-financial liberalization (Jan.1991-Jan. 2002) periods. The mean of the returns is negative in all periods, and increases over time, butthe mean in the second period is higher than the mean in the first period. However, the standard deviation(as a measure of risk) does not decrease significantly; it in fact increases. The period Jan. 1991-Jan. 2002displays a higher mean return but with a higher level of standard deviation (risk) for both daily andweekly data.

With the inauguration of a new government on June 24, 1996, the stock market started rising withoutany connection to company fundamentals. The DSEI index grew from 804 on May 31, 1996 to an all timehigh of 3596 on November 17, 1996, and then fell to 622.28 on April 16, 1998. In order to exclude

7“irrational exuberance” from the DSE data during the speculative run-up of the DSE index, we omit thedata from July 1, 1996 - December 31, 1996, and ran all descriptive statistics again. For both daily andweekly data, the exclusion of this period increases the standard deviation of the second and the totalperiod, but it does not affect the mean return for that period.

The distribution of the stock returns is skewed to the left for both frequencies of data. The exclusion ofthe period (01 July 1996- 31 December 1996) creates a large left skewness in the data. Further, a largeexcess (positive) kurtosis is found for the full and sub samples. The returns in DSE index have muchthicker distribution tails than the normal distribution. This kurtosis becomes larger when we exclude theabnormal period.

Thus, the Dhaka Stock Exchange Index (DSEI) shows negative skewness, excess kurtosis anddeviation from normality, which are consistent with the findings of other countries. Fama (1965) showsthat the distribution of both daily and monthly returns of Dow Jones and NYSE indices depart fromnormality, and are skewed, leptokurtic, and volatility clustered. Campbell, Lo and Mackinlay (1997)conclude that daily U.S. stock indices show negative skewness and positive excess kurtosis. Bekaert et al.(1998) provide evidence that 17 out of 20 emerging countries examined (the sample does not includeBangladesh) have positive skewness and 19 of 20 have excess kurtosis, so that normality was rejected fora majority of the sample countries.

5. Time Varying Risk-Return Behavior of Dhaka Stock Exchange

5.1. Autocorrelation and Non-Synchronous Trading:

Table 3 presents the empirical results of the volatility of stock returns. The equity return is calculatedas the log difference of the DSE stock price index: Rt = ln (Pt) - ln(Pt-1). The AR(1) coefficient in Table 3shows mixed results. For the daily returns, the AR(1) coefficients are negative and statisticallyinsignificant at the 5% level when used the pre-financial liberalization samples (columns 2 and 6). For thepost-liberalization samples (columns 4 and 8), the AR(1) coefficients are positive and statisticallysignificant at 1% level. The results are consistent across samples for the daily returns and in all cases theLjung-Box test indicates that the residuals are not free of autocorrelation. As for the weekly data, threeout of four cases produce statistically insignificant AR(1) coefficients at the 5% level. The AR(1)coefficient is negative when we exclude the observations from July 1996 through Dec. 1996 and ispositive otherwise.

2 Formerly Peregrine Capital Limited (PCL), Bangladesh Branch. We are thankful to A.A.M.M. Shamsuzzaman andShaira Wahid for making the data available to us. The data is available from the corresponding author upon request.

8 The different sign of the AR(1) coefficients for different samples can be attributed to the spuriouseffects associated with nonsynchronous trading. As explained in Campbell, Lo and MacKinlay (p. 129,1997), the presence of nonsynchronous trading induces geometrically declining negative serial correlationin observed individual security returns that have nonzero means. In contrast, nonsynchronous trading thatproduces positive serial correlation in observed portfolio returns, yield an AR(1) for the observed returnprocess. These serial autocorrelations (negative and positive) are induced by non-enforcement ofregulation and weak supervision of the stock exchange. Moreover, there are a large number of non-actively traded shares in the stock market. Finally, the supply of securities is also very limited. Onlyrecently has the government allowed the issuance of mutual funds by private investment houses. Theobserved time dependence of stock returns in Bangladesh may be the result of a lack of merchant andinvestment banks, which tend to promote equity research and increase the speed of adjustment to newinformation.

5.2. Volatility and Returns in Dhaka Stock Exchange:

We present the results for volatility and risk in Table 3. We divide the sample into the pre- and postliberalization sub-periods, and also report results after excluding the July 1, 1996 through December 31,1996 period (see section 4). The hypothesis that volatility is a significant determinant of stock returns isconfirmed as the parameter δ1, capturing the influence of volatility on stock returns, is mostly statisticallyinsignificant at the 10% level for both daily and weekly returns. For daily returns, the risk-returncoefficient is mostly positive, while it is mostly negative for weekly returns. The regression coefficient ofthe risk-return parameter is found significant only for the pre-liberalization sample with daily returns(column 6). Empirical applications to data found mixed results regarding the sign and statisticalsignificance of the risk-return parameter. Elyasiani and Mansur’s (1998) estimates on U.S. data werenegative and significant. Chou (1988) and Poterba and Summers (1986) document that excess returns onthe daily S&P index, weekly NYSE returns and U.K stock indices were positive and significant. Foremerging markets, Thomas (1995) finds a positive but insignificant risk-return parameter for the BombayStock Exchange, and Mecagni and Sourial (1999) find a positive and significant risk-return parameter forEgyptian stock markets. Engle et al. (1987) and Bollerslev et al. (1992) state that the sign and magnitude of the risk-returnparameters depend on the investor’s utility function and risk preference, and the supply of securities underconsideration. Glosten et al. (1993) discuss special circumstances that would make it possible to observe anegative correlation between current returns and current measures of risk. Investors may not demand highrisk premia if they are better able to bear risk at times of particular volatility. Moreover, if the future

9seems risky investors may want to save more in the present thus lowering the need for larger premia.And, if transferring income to the future is risky and the opportunity of investment in a risk-free asset isabsent, then the price of a risky asset may increase considerably, hence reducing the risk premium.According to Glosten et al. (1993), both positive and negative relationships between current returns andcurrent variances (risk) are possible. The significance of α1 parameters in the model indicates the tendency of the shocks to persist. For thedaily returns the measure of volatility persistence, α1+β1, coefficient is greater than unity and statisticallysignificant for the pre-liberalization samples implying nonstationarity of conditional variance whichmight have resulted due to the presence of serial correlation in the returns (Bera and Higgins (1993)).However, when regressed for the post-financial liberalization period (Jan. 1991-Jan. 2002), the sum of theARCH and GARCH coefficient, α1+β1, turns out to be less than unity and statistically significant at 1%level. When observations for the period July 1996 through Dec. 1996 are excluded the volatility of stockreturns for both daily and weekly data increases. This is consistent with the findings of section 4 (seeTables 2a and 2b). In contrast, the sum of ARCH and GARCH coefficient always exceeds unity forweekly returns suggesting higher volatility associated with low frequency of the data. The above results indicate that the tendency for a volatility response to shocks displays a longmemory. These results confirm the time varying risk in the stock returns in Bangladesh. The conditionalvariance changes over time. These results show that periods of relatively high (or low) volatility are foundto be time-dependent. The opening up of the stock market neither reduces time-varying risk nor reducesvolatility persistence over time.

5.3. Lock-in, Circuit Breaker, and Stock Market Volatility

In order to curb speculation in the equity market, the government introduced a system of lock-in forprimary securities on February 11, 1995. Under this lock-in provision, foreign investors are not allowedto trade in initial public offering3 for a year. However, this lock-in system was abolished on July 08, 1996to encourage foreign investment in the equity market. Under this new rule, foreign investors do not faceany lock-in period either for primary or secondary shares. However, the Bangladeshi sponsors face a 3-year lock-in period in sponsor’s equity, while foreign investors do not face such a lock-in period. Forsecondary shares, an investor has to register with the Securities and Exchange Commission if he acquiresat least 10% of any publicly listed company equity.

3 On average about 8-10 new shares are introduced in the Dhaka Stock Exchange through initial public offering(IPO) each year.

10 The circuit breaker system was introduced within three months of the stock market bubble in 1996.The circuit breaker system is explained in appendix A. Daily price limits may truncate the distribution ofprice changes for individual stocks, and produce irregularly observed or missing data as the equilibriumprice is no longer observable when the price limit becomes binding. Price limits may represent a barrier tomarket clearing, and prevent, rather than enhance, the price discovery process by delaying price changesthat are the result of development of underlying stock fundamentals. Price limits may also create liquidityproblems, to the extent that buyers (sellers) are unwilling to enter the market as a result of furtheranticipated price decreases (increases). The distortions may also make price limits self-fulfilling. Forinstance, the fears of illiquidity or of remaining locked into an investment position may increase earlytrading, as participants recognize the risk of being unable to trade when prices move closer to the limit.Trading on the other hand may be impaired if market participants act to prevent the limit from being hit,for instance as they recognize that their ability to trade or modify their positions could then be adverselyaffected. However, price limits may provide markets with a cooling off period preventing investors frompanicking, and favoring a substantial reduction in volatility, particularly in periods of significantuncertainty that may lead to market overreaction to news (see Ma, Rao and Sears (1989) Lauterbach andBen-Zion (1993) and Chowdhury and Nanda (1998)). In order to test the impact of circuit-breakers and lock-ins on volatility we estimate a GARCH(1,1)model so that the effect can be tested directly. Consequently, we introduce a dummy variable for circuit-breakers and lock-ins as an exogenous variable in the conditional variance equation by modifyingEquation 6:(7) ht = α 0 + α 1ε t2−1 + β 1 ht −1 + δ Di + ε t

where Di is zero-one dummy representing the exogenous variables. We first examine the imposition(February 11, 1995) and then the repeal (July 8, 1996) of lock-in period for foreign investors on thevolatility of the DSE returns. Then we examine the impact of the imposition of the circuit breaker in twophases-first a 10% and second a 5% on the conditional volatility of stock returns. The results are reportedin Tables 4a-4d. For both daily and weekly returns the dummy variable D1 (Lock-in versus Lock-out) isfound to have a negative effects in most cases but statistically insignificant at the 5% level. When theexuberance observations are excluded from the full sample (Table 4c), the coefficient of D1 becomespositive for both daily and weekly returns. Besides, the presence of dummy variable D1 produces the sumof ARCH and GARCH coefficients to be greater than unity for weekly returns in all cases and two out offour cases for the daily returns. Both ARCH-LM and Ljung-Box test indicate that the residuals are notfree from autocorrelations.

11 The dummy variable D2 (introducing the first circuit breaker of 10%) is found to have both positiveand negative effects for daily returns and mostly positive effects for weekly returns. For the daily returnsthe coefficient of D2 is positive and significant at the 10% level for the post-financial liberalizationssample (Table 4b) implying that the volatility increases with the imposition of this restriction. However,for both daily and weekly returns, when the exuberance observations are excluded from the sample, theregression coefficient of D2 becomes negative but statistically significant at 1% level indicating thatvolatility decreases with the imposition of this restriction. The result is quite consistent in the sense thatthe restriction (D2) has significant impact on volatility (positive and negative) for the post-liberalizationperiod. The dummy variable D3 (introducing the second circuit breaker of 5%) is found to have a significantnegative effect in three out of four cases for the daily returns and only one case for weekly returns,implying that the imposition of this restriction reduces volatility of the realized returns significantly. Thedummy variable D4 (the third circuit breaker of 10%) has no significant impact on the weekly returns butit its effect on daily returns is found negative and significant in two out of four cases. The regressioncoefficient of D4 consistently influences (downward) the volatility of the pre-liberalization samples.Finally, the dummy variable D5 (introducing the different circuit breakers, thus giving combined effect)echoes the results of D4. It too has no effect on weekly returns but affects significantly the pre-liberalization samples of daily returns. In summary, although lock-in did not have any significant impact on stock volatility, circuit breakersseem to have some influences on the volatility of both daily and weekly returns for both pre- and post-liberalization samples. The results are quite consistent across samples and sign of coefficients. All dummyvariables seem to affect the volatility of realized returns negatively and the results are quite robust fordaily returns.

6. Summary and Policy Implications

This paper has empirically investigated the return behavior of the Dhaka Stock Exchange Index (DSEI),the time-varying risk-return relationship within a GARCH-type framework, and the persistence of shocksto volatility. The Bangladesh capital market has gone through major changes since 1990s during whichthe stock market was opened to foreign investment. The DSE returns show negative skewness, excess kurtosis and deviation from normality. The DSEvolatility tends to change over time, and is serially correlated. In addition, the DSE returns displaysignificant serial correlation indicating stock market inefficiency. The results also show a significantrelationship between conditional volatility and the DSE stock returns, but the risk-return parameter is

12found to be both negative and positive. While the negative sign of risk-return coefficient is not consistentwith portfolio theory, it is theoretically possible in emerging markets as investors may not demand higherrisk premia if they are better able to bear risk at times of particular volatility (Glosten, Jagannathan andRunkle (1993)). While the lock-in did not have any overall impact on stock volatility, the imposition ofthe circuit breakers seems to have significant influence over the volatility of realized returns. The negative risk-return relationship in the DSEI may result from the additional tax treatment ofinterest income and dividend income, and weak corporate profit performance. Besides, informationasymmetry may play a crucial in influencing the distribution of returns among investors. Also, a numberof companies do not hold annual general meetings as stipulated in company guidelines, nor they dodeclare regular dividends or invest the retained earnings in value maximizing investments. The processing of new information in Bangladesh is rather weak, and may result from the persistentlylarge number of non-actively traded shares, and the limited role of mutual funds and professionallymanaged investment and broker houses. To improve the operation of capital market the governmentshould emphasize a policy of timely disclosure and dissemination of information to the stockholders andinvestors on the performance of listed companies.

Indicators Dhaka Stock Exchange

(As on June 2003)No. of companies 241No. of mutual funds 10No. of debentures 9Total No. of Listed Securities 260

(Fig. In million)No. of shares of all listed companies 972.72No. of certificates of all listed mutual funds 72.25No. of debentures of all listed debentures 0.635Total No. of Tradable Securities 1045.60

Skewness -1.648 -0.714 -22.076 -21.628

Jarque-Berac 0.00 0.00 0.00 0.00

(p-value)ADF unit root 0.00 0.00 0.00 0.00testd (p-value)Observation (N) 4137 2997 3988 2848Notes: * indicates significance at the 1% level.a. t=(S′-0)/se(S′), where se(S′)=square root(6/N).b. t=(K′-3)/se(K′), where se(K′)=square root(24/N).c. Under the null hypothesis of a normal distribution, the Jarque-Bera statistic is distributed as χ2 with 2 degrees of freedom. The p-values indicate rejection of the null hypothesis, hence deviation form normality.d. Under the null hypothesis of a unit root, for the ADF test, the lag length has been chosen using Akaike Information Criterion. P-values are calculated based on MacKinnon (1996). The results indicate that the weekly equity return is stationary.

The unusual and abnormal price fluctuation raised the Price Index to unprecedented heights. In 1996the Securities and Exchange Commission, in order to protect the interest of the investors, introducedCircuit Breaker System. The guidelines of Circuit Breaker System are given below:

1. Standard upward and downward price limits over the previous days’ market price applicable for each market day are as follows:

2. In case of new issue, free trade may be allowed for first 5 (five) consecutive market days and after that above limit will be applicable.

3. In case of receipt of any price sensitive information like right issues, bonus issue and dividend from the listed company, free trade may be allowed for subsequent 3 (three) consecutive market days, and after that above limits will be applicable.

4. In case securities not traded for previous consecutive 30 market days, free trade may be allowed for subsequent 3 (three) consecutive market days and after that above limits will be applicable.